Stephen E. Fancsali

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Susan R BermanUniversity of Pittsburgh, UPMC, Carnegie Learning, Inc.Verified email at carnegielearning.com
Michael (Mikhail) YudelsonStaff Data Scientist at Chegg, Inc.Verified email at chegg.com
Vincent AlevenProfessor of Human-Computer Interaction, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Vasile RusThe University of MemphisVerified email at memphis.edu
Ambarish JoshiCarnegie Learning, Research AnalystVerified email at carnegielearning.com
John StamperHuman-Computer Interaction Institute, Carnegie Mellon UniversityVerified email at stamper.org
Guoguo ZhengUniversity of GeorgiaVerified email at uga.edu
Kenneth HolsteinCarnegie Mellon UniversityVerified email at cs.cmu.edu
Ryan C SchwiebertCenter of Ring Theory and its ApplicationsVerified email at ohio.edu
R. Charles MurrayVerified email at chasmurray.com
Bruce M. McLarenProfessor, Carnegie Mellon University, U.S.Verified email at cs.cmu.edu
David DanksProfessor of Data Science & Philosophy; UC San DiegoVerified email at ucsd.edu
Richard ScheinesDean, Dietrich College of Humanities and Social Sciences, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Nabin MaharjanData Scientist, Audible Inc. (an Amazon Company)Verified email at audible.com
Lasang J.TamangPhD, University of MemphisVerified email at memphis.edu
Charles Chuankai ZhangUniversity of MinnesotaVerified email at umn.edu
J Derek Lomas PhDIndustrial Design Engineering, Delft University of TechnologyVerified email at tudelft.nl
Nirmal PatelPlaypower LabsVerified email at playpowerlabs.com
Matthew L BernackiAssociate Professor, University of North Carolina, Chapel HillVerified email at unc.edu
Timothy Nokes-MalachAssociate Professor of Psychology, University of PittsburghVerified email at pitt.edu

Stephen E. Fancsali

Vice President of Data Science, Carnegie Learning, Inc.

Verified email at carnegielearning.com - Homepage

Educational Data Science Learning Analytics Causal Discovery Machine Learning


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How Mastery Learning Works at Scale S Ritter, M Yudelson, SE Fancsali, SR Berman Proceedings of the Third (2016) ACM Conference on Learning @ Scale, 71-79, 2016	98	2016
Predicting Standardized Test Scores from Cognitive Tutor Interactions S Ritter, A Joshi, SE Fancsali, T Nixon Sixth International Conference on Educational Data Mining, 2013	45	2013
Causal Discovery with Models: Behavior, Affect, and Learning in Cognitive Tutor Algebra SE Fancsali 7th International Conference on Educational Data Mining (EDM 2014), 28-35, 2014	42	2014
Revealing the learning in learning curves RC Murray, S Ritter, T Nixon, R Schwiebert, RGM Hausmann, B Towle, ... Artificial Intelligence in Education: 16th International Conference, AIED …, 2013	36	2013
Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics G Zheng, SE Fancsali, S Ritter, S Berman Journal of Learning Analytics 6 (2), 153–174, 2019	32	2019
Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics SE Fancsali, S Ritter Fourth International Conference on Learning Analytics And Knowledge, 73-77, 2014	32	2014
Optimal and Worst-Case Performance of Mastery Learning Assessment with Bayesian Knowledge Tracing SE Fancsali, T Nixon, S Ritter Sixth International Conference on Educational Data Mining, 2013	30	2013
Using embedded formative assessment to predict state summative test scores SE Fancsali, G Zheng, Y Tan, S Ritter, SR Berman, A Galyardt Proceedings of the 8th International Conference on Learning Analytics and …, 2018	28	2018
Better Data Beat Big Data MV Yudelson, SE Fancsali, S Ritter, SR Berman, T Nixon, A Joshi 7th International Conference on Educational Data Mining (EDM 2014), 205-208, 2014	23	2014
Intelligent Instructional Hand Offs SE Fancsali, MV Yudelson, SR Berman, S Ritter Educational Data Mining 2018, 198-207, 2018	22	2018
Data-driven causal modeling of “gaming the system” and off-task behavior in Cognitive Tutor Algebra SE Fancsali NIPS 2013 Workshop on Data-Driven Education, 2013	21	2013
Variable construction for predictive and causal modeling of online education data SE Fancsali Proceedings of the 1st International Conference on Learning Analytics and …, 2011	21	2011
An Analysis of Human Tutors' Actions in Tutorial Dialogues V Rus, N Maharjan, LJ Tamang, M Yudelson, S Berman, SE Fancsali, ... Thirtieth International Florida Artificial Intelligence Research Society …, 2017	20	2017
Carnegie Learning’s Adaptive Learning Products S Ritter, R Carlson, M Sandbothe, SE Fancsali 8th International Conference on Educational Data Mining, 2015	20	2015
Comorbid science? D Danks, S Fancsali, C Glymour, R Scheines Behavioral and Brain Sciences 33 (2-3), 153-155, 2010	20	2010
Early Detection of Wheel Spinning: Comparison across Tutors, Models, Features, and Operationalizations. C Zhang, Y Huang, J Wang, D Lu, W Fang, J Stamper, S Fancsali, ... EDM 2019, 2019	18	2019
UpGrade: An Open Source Tool to Support A/B Testing in Educational Software S Ritter, A Murphy, S Fancsali, V Fitkariwala, N Patel, JD Lomas L@S Workshop on Educational A/B Testing at Scale, 2020	16	2020
Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial Results. S Fancsali EDM, 238-239, 2012	16	2012
Constructing categories: Moving beyond protected classes in algorithmic fairness C Belitz, J Ocumpaugh, S Ritter, RS Baker, SE Fancsali, N Bosch Journal of the Association for Information Science and Technology, 2022	15	2022
MATHia X: The Next Generation Cognitive Tutor S Ritter, SE Fancsali 9th International Conference on Educational Data Mining, 624-625, 2016	15	2016

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